Microsoft open-sources SynapseML for developing AI pipelines

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Microsoft today announced the release of SynapseML (previously MMLSpark), an open source library designed to simplify the creation of machine learning pipelines. With SynapseML, developers can build “scalable and intelligent” systems for solving challenges across domains, including text analytics, translation, and speech processing, Microsoft says.

“Over the past five years, we have worked to improve and stabilize the SynapseML library for production workloads. Developers who use Azure Synapse Analytics will be pleased to learn that SynapseML is now generally available on this service with enterprise support [on Azure Synapse Analytics],” Microsoft software engineer Mark Hamilton wrote in a blog post.

Scaling up AI

Building machine learning pipelines can be difficult even for the most seasoned developer. For starters, composing tools from different ecosystems requires considerable code, and many frameworks aren’t designed with server clusters in mind.

Despite this, there’s increasing pressure on data science teams to get more machine learning models into use. While AI adoption and analytics continue to rise, an estimated 87% of data science projects never make it to production. According to Algorithmia’s recent survey, 22% of companies take between one and three months to deploy a model so it can deliver business value, while 18% take over three months.

SynapseML aims to address the challenge by unifying existing machine learning frameworks and Microsoft-developed algorithms in an API, usable across Python, R, Scala, and Java. SynapseML enables developers to combine frameworks for use cases that require more than one framework, such as search engine creation, while training and evaluating models on resizable clusters of computers.

As Microsoft explains on the project’s website, SynapseML expands Apache Spark, the open source

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